A Zero Downtime Setup for Models: How and Why
Discover techniques for achieving zero-downtime model updates and handling failures in production environments. Learn a robust setup to ensure continuous availability and reliability of deployed models.
Continuously Improving Large Scale Recommenders with MLOps Tools and Practices
Leverage MLOps tools and practices to operationalize and continuously improve recommendation systems at scale using NVIDIA Merlin and Kubeflow on Google Kubernetes Engine.
Running ML Inference Services in Shared Hosting Environments
Optimize ML inference in shared hosting: Learn to boost latency, throughput, and resource utilization through queue management and OpenMP tuning.
SLA-Aware Machine Learning Inference Serving on Serverless Computing Platforms
Explore adaptive batching for efficient ML inference serving on serverless platforms. Learn to optimize costs and ensure SLA compliance using MLProxy for improved performance.
Shopify's Machine Learning Platform Journey: Building Merlin with Open Source Tools
Explore Shopify's journey in building Merlin, an open-source ML platform. Gain insights into the architecture, technologies, and tools used to scale machine learning work effectively.
Scale and Accelerate Distributed Model Training in Kubernetes Clusters
Orchestrate and accelerate distributed deep learning workloads across multiple GPUs and nodes using Kubernetes, Kubeflow PytorchJob, RDMA, RoCE, and SRIOV for near-linear performance scaling.
MLOps for Deep Learning: Drift Detection and Efficient Retraining in Model Serving
Explore advanced MLOps techniques for deep learning, including drift detection, efficient retraining, and continuous deployment. Enhance model reliability and performance in production environments.
Human Centric MLOps - Why Productivity Matters the Most
Explore human-centric MLOps and its impact on productivity. Learn how focusing on people, not just systems, can revolutionize machine learning operations and drive innovation.
Accelerating Deployment of AI with MLOps Orchestration
Explore MLOps orchestration to simplify AI deployment using Kubernetes and open-source tools. Learn to build efficient ML pipelines, from data preparation to model monitoring, accelerating business value across various use cases.
Critical Use of MLOps in Finance Using Cloud-Managed ML Services That Scale
Leverage MLOps to improve quality and consistency of machine learning solutions in finance. Accelerate model experimentation, deployment, and tracking using cloud-managed services for efficient scaling.
MLOps Beyond Training - Simplifying and Automating the Operational Pipeline
Explore a production-first approach to MLOps, automating operational pipelines for measurable ROI. Learn to design continuous workflows, integrate components, and simplify the transition from research to scalable production.
Top 5 Lessons Learned in Helping Organizations Adopt MLOps Practices
Discover key insights and strategies for implementing MLOps practices at scale. Learn common challenges and effective mitigation techniques for successful organizational adoption.
Implementing MLOps Practices on AWS using Amazon SageMaker
Implement ML pipelines with CI/CD practices using SageMaker Pipelines. Gain hands-on experience in MLOps, incorporating DevOps principles into machine learning workflows for scalable and efficient model deployment.
Accelerating Transformers with Hugging Face Optimum and Infinity
Explore techniques to accelerate Transformer models using Hugging Face's Optimum library and Infinity solution for millisecond-scale latencies in production environments.
Shopify's Machine Learning Platform Journey: Building Merlin with Open Source Tools
Explore Shopify's journey in building Merlin, an open-source ML platform. Gain insights into the architecture, technologies, and tools used to scale machine learning work effectively.
Hands-on Workshop: Create Machine Learning Retraining Pipelines
Develop ML retraining pipelines using Kubernetes and Kubeflow. Master advanced techniques for efficient model updates and deployment in production environments.
How to MLEM Your Models to Production
Explore MLEM, an open-source tool for storing, accessing, packaging, and deploying machine learning models efficiently in various production scenarios.
Hands-on: A Beginner-Friendly Crash Course to Kubernetes
Découvrez les bases de Kubernetes et des applications Cloud Native. Explorez l'orchestration de conteneurs et le déploiement d'applications dans un environnement cloud moderne.
How to Reduce ML Computing Costs: Building Efficient Multi-Cloud Infrastructure
Discover cost-effective ML infrastructure strategies, including hybrid cloud architectures, spot instances, and fractional GPUs. Learn to optimize computing costs by up to 83% across multiple environments.
MLOps on Highly Sensitive Data - Enhancing Security and Compliance
Enhance security and compliance in MLOps for sensitive data using Kubernetes, blockchain tokenization, and confidential computing. Learn from a life sciences case study.
A Guide to Building a Continuous MLOps Stack with GCP and Superwise
Dive into MLOps CI/CD pipeline automation with GCP and Superwise. Learn to build, deploy, and monitor continuous ML pipelines, focusing on automations and production-first insights for issue detection and resolution.